1 CE 530 Molecular Simulation Lecture 18 Free-energy calculations David A. Kofke Department of Chemical Engineering SUNY Buffalo

2 Free-Energy Calculations  Uses of free energy Phase equilibria Reaction equilibria Solvation Stability Kinetics  Calculation methods Free-energy perturbation Thermodynamic integration Parameter-hopping Histogram interpolation

3 Ensemble Averages  Simple ensemble averages are of the form  To evaluate: sample points in phase space with probability  (  ) at each point, evaluate M(  ) simple average of all values gives  Previous example mean square distance from origin in region R sample only points in R, average r 2  Principle applies to both MD and MC 

4 Ensemble Volumes  Entropy and free energy relate to the size of the ensemble e.g., S = k ln  (E,V,N)  No effective way to measure the size of the ensemble no phase-space function that gives size of R while sampling only R imagine being place repeatedly at random points on an island what could you measure at each point to determine the size of the island?  Volume of ensemble is numerically unwieldy e.g. for 100 hard spheres  = 0.1,  = 5   = 0.5,  = 3  10 7  = 0.9,  = 5   Shape of important region is very complex cannot apply methods that exploit some simple geometric picture  = number of states of given E,V,N 

5 Reference Systems  All free-energy methods are based on calculation of free- energy differences  Example volume of R can be measured as a fraction of the total volume sample the reference system keep an average of the fraction of time occupying target system what we get is the difference  Usefulness of free-energy difference it may be the quantity of interest anyway if reference is simple, its absolute free energy can be evaluated analytically e.g., ideal gas, harmonic crystal 

6  Chemical potential is an entropy difference  For hard spheres, the energy is zero or infinity any change in N that does not cause overlap will be change at constant U  To get entropy difference simulate a system of N+1 spheres, one non-interacting “ghost” occasionally see if the ghost sphere overlaps another record the fraction of the time it does not overlap  Here is an applet demonstrating this calculation Here is N+1 N Hard Sphere Chemical Potential

7 Free-Energy Perturbation  Widom method is an example of a free-energy perturbation (FEP) technique  FEP gives free-energy difference between two systems labeled 0, 1  Working equation  0 1 Free-energy difference is a ratio of partition functions

8 Free-Energy Perturbation  Widom method is an example of a free-energy perturbation (FEP) technique  FEP gives free-energy difference between two systems labeled 0, 1  Working equation  0 1 Add and subtract reference-system energy

9 Free-Energy Perturbation  Widom method is an example of a free-energy perturbation (FEP) technique  FEP gives free-energy difference between two systems labeled 0, 1  Working equation  0 1 Identify reference- system probability distribution

10 Free-Energy Perturbation  Widom method is an example of a free-energy perturbation (FEP) technique  FEP gives free-energy difference between two systems labeled 0, 1  Working equation  Sample the region important to 0 system, measure properties of 1 system  0 1 Write as reference- system ensemble average

11 Chemical potential  For chemical potential, U 1 - U 0 is the energy of turning on the ghost particle call this u t, the “test-particle” energy test-particle position may be selected at random in simulation volume for hard spheres, e -  u t is 0 for overlap, 1 otherwise then (as before) average is the fraction of configurations with no overlap  This is known as Widom’s insertion method

12 Widom’s Method. API

13 Widom’s Method. Java Code public class MeterWidomInsertion extends Meter /** * Performs insertion average used in chemical-potential calculation */ public double currentValue() { double sum = 0.0; //zero sum for insertion average phase.addMolecule(molecule,speciesAgent); //place molecule in phase for(int i=nInsert; i>0; i--) { //perform nInsert insertions molecule.translateTo(phase.randomPosition()); //select random position double u = phase.potentialEnergy.currentValue(molecule); //compute energy if(u < Double.MAX_VALUE) //add to test-particle average sum += Math.exp(-u/(phase.integrator().temperature())); } phase.deleteMolecule(molecule); //remove molecule from phase if(!residual) sum *= speciesAgent.moleculeCount/phase.volume(); //multiply by Ni/V return sum/(double)nInsert; //return average } //Method to identify species for chemical-potential calculation by this meter //Normally called only once in a simulation public void setSpecies(Species s) { species = s; molecule = species.getMolecule(); if(phase != null) speciesAgent = species.getAgent(phase); }

14 Deletion Method  The FEP formula may be used also with the roles of the reference and target system reversed sample the 1 system, evaluate properties of 0 system  Consider application to hard spheres e -  u t is infinity for overlap, 0 otherwise but overlaps are never sampled true average is product of 0   technically, formula is correct in practice simulation average is always zero method is flawed in application many times the flaw with deletion is not as obvious as this  0 1 Original: 0  1Modified: 1  0

15 Other Types of Perturbation  Many types of free-energy differences can be computed  Thermodynamic state temperature, density, mixture composition  Hamiltonian for a single molecule or for entire system e.g., evaluate free energy difference for hard spheres with and without electrostatic dipole moment  Configuration distance/orientation between two solutes e.g, protein and ligand  Order parameter identifying phases order parameter is a quantity that can be used to identify the thermodynamic phase a system is in e.g, crystal structure, orientational order, magnetization

16 General Numerical Problems  Sampling problems limit range of FEP calculations  Target system configurations must be encountered when sampling reference system  Two types of problem arise first situation is more common although deletion FEP provides an avoidable example of the latter  0 1 target-system space very small  0 1 target system outside of reference

17 Staging Methods  Multistage FEP can be used to remedy the sampling problem define a potential U w intermediate between 0 and 1 systems evaluate total free-energy difference as  Each stage may be sampled in either direction yielding four staging schemes choose to avoid deletion calculation  0 1 W Use staged insertionUse umbrella sampling  0 1 W  0 1 Use Bennett’s method W

18 Example of Staging Method  Hard-sphere chemical potential  Use small-diameter sphere as intermediate In first stage, measure fraction of time random insertion of small sphere finds no overlap In second stage, small sphere moves around with others. Measure fraction of time no overlap is found when it is grown to full-size sphere 

19 Multiple Stages  0 1 W1W1 W2W2 W3W3  0 1 W1W1 W2W2 W3W3  0 1 W1W1 W3W3 W2W2 Multistage insertionMultistage umbrella samplingMultistage Bennett’s method

20 Non-Boltzmann Sampling  The FEP methods are an instance of a more general technique that aims to improve sampling  Unlike biasing methods, improvement entails a change in the limiting distribution  Apply a formula to recover the correct average  W 0 0

21 Thermodynamic Integration 1.  Thermodynamics gives formulas for variation of free energy with state  These can be integrated to obtain a free-energy difference derivatives can be measured as normal ensemble averages this is usually how free energies are “measured” experimentally

22 Thermodynamic Integration 2.  TI can be extended to follow uncommon (or unphysical) integration paths much like FEP, can be applied for any type of free-energy change  Formalism let be a parameter describing the path the potential energy is a function of ensemble formula for the derivative then

23 Thermodynamic Integration Example  The soft-sphere pair potential is given by  Softness and range varies with n large n limit leads to hard spheres small n leads to Coulombic behavior  Thermodynamic integration can be used to measure free energy as a function of softness s = 1/n Integrand is Exhibits simplifying behavior because  n is the only potential parameter

24 Parameter Hopping. Theory  View free-energy parameter as another dimension in phase space  Partition function  Monte Carlo trials include changes in  Probability that system has  0 or = 1 

25 Parameter Hopping. Implementation  Monte Carlo simulation in which -change trials are attempted  Accept trials as usual, with probability min[1,e -  U ]  Record fractions f 0, f 1 of configurations spent in  0 and = 1  Free energy is given by ratio  In practice, system may spend almost no time in one of the values Can apply weighting function w( ) to encourage it to sample both Accept trials with probability min[1,(w n /w o ) e -  U ] Free energy is Good choice for w has f 1 = f 0  Multivalue extension is particularly effective takes on a continuum of values

26 Summary  Free energy calculations are needed to model the most interesting physical behaviors All useful methods are based on computing free-energy difference  Four general approaches Free-energy perturbation Thermodynamic integration Parameter hopping Distribution-function methods  FEP is asymmetric Deletion method is awful  Four approaches to basic multistaging Umbrella sampling, Bennett’s method, staged insertion/deletion  Non-Boltzmann methods improve sampling